A multi‐stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four‐view mammograms

Jones, Meredith; Sadeghipour, Negar; Chen, Xuxin; Islam, Warid; Zheng, Bin

doi:10.1002/mp.16419

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…28 This current review extracted and organized the data in tabular form and summarized the use of MG in the diagnosis of breast cancer (Table 1). 2,4,5,7,9,24,29–79…”

Section: Introductionmentioning

confidence: 99%

Machine learning and new insights for breast cancer diagnosis

Guo,

Zhang,

Yuan

et al. 2024

J Int Med Res

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Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

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“…28 This current review extracted and organized the data in tabular form and summarized the use of MG in the diagnosis of breast cancer (Table 1). 2,4,5,7,9,24,29–79…”

Section: Introductionmentioning

confidence: 99%

Machine learning and new insights for breast cancer diagnosis

Guo,

Zhang,

Yuan

et al. 2024

J Int Med Res

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Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning–Based Computer-Aided Diagnosis Schemes in Breast Mass Classification

Jones,

Zhang,

Faiz

et al. 2024

J Digit Imaging. Inform. med.

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CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound

Wang,

Liu

et al. 2024

Tomography

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Background/Objectives: Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagnostic accuracy. This study aims to enhance breast cancer detection through a cross-modality fusion approach combining mammography and ultrasound imaging, using advanced convolutional neural network (CNN) architectures. Materials and Methods: Breast images were sourced from public datasets, including the RSNA, the PAS, and Kaggle, and categorized into malignant and benign groups. Data augmentation techniques were used to address imbalances in the ultrasound dataset. Three models were developed: (1) pre-trained CNNs integrated with machine learning classifiers, (2) transfer learning-based CNNs, and (3) a custom-designed 17-layer CNN for direct classification. The performance of the models was evaluated using metrics such as accuracy and the Kappa score. Results: The custom 17-layer CNN outperformed the other models, achieving an accuracy of 0.964 and a Kappa score of 0.927. The transfer learning model achieved moderate performance (accuracy 0.846, Kappa 0.694), while the pre-trained CNNs with machine learning classifiers yielded the lowest results (accuracy 0.780, Kappa 0.559). Cross-modality fusion proved effective in leveraging the complementary strengths of mammography and ultrasound imaging. Conclusions: This study demonstrates the potential of cross-modality imaging and tailored CNN architectures to significantly improve diagnostic accuracy and reliability in breast cancer detection. The custom-designed model offers a practical solution for early detection, potentially reducing false positives and false negatives, and improving patient outcomes through timely and accurate diagnosis.

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A multi‐stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four‐view mammograms

Cited by 3 publications

References 35 publications

Machine learning and new insights for breast cancer diagnosis

Machine learning and new insights for breast cancer diagnosis

Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning–Based Computer-Aided Diagnosis Schemes in Breast Mass Classification

CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound

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